Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914861910327296 |
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| author | Zeng, Boshen Chen, Sian Liu, Xinxin Chen, Changhong Deng, Bin Wang, Xiaoxu Gao, Zhifeng Zhang, Yuzhi E, Weinan Zhang, Linfeng |
| author_facet | Zeng, Boshen Chen, Sian Liu, Xinxin Chen, Changhong Deng, Bin Wang, Xiaoxu Gao, Zhifeng Zhang, Yuzhi E, Weinan Zhang, Linfeng |
| contents | Advancements in lithium battery technology heavily rely on the design and engineering of electrolytes. However, current schemes for molecular design and recipe optimization of electrolytes lack an effective computational-experimental closed loop and often fall short in accurately predicting diverse electrolyte formulation properties. In this work, we introduce Uni-ELF, a novel multi-level representation learning framework to advance electrolyte design. Our approach involves two-stage pretraining: reconstructing three-dimensional molecular structures at the molecular level using the Uni-Mol model, and predicting statistical structural properties (e.g., radial distribution functions) from molecular dynamics simulations at the mixture level. Through this comprehensive pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, which significantly enhances its predictive capability. As a result, Uni-ELF substantially outperforms state-of-the-art methods in predicting both molecular properties (e.g., melting point, boiling point, synthesizability) and formulation properties (e.g., conductivity, Coulombic efficiency). Moreover, Uni-ELF can be seamlessly integrated into an automatic experimental design workflow. We believe this innovative framework will pave the way for automated AI-based electrolyte design and engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06152 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design Zeng, Boshen Chen, Sian Liu, Xinxin Chen, Changhong Deng, Bin Wang, Xiaoxu Gao, Zhifeng Zhang, Yuzhi E, Weinan Zhang, Linfeng Chemical Physics Artificial Intelligence Advancements in lithium battery technology heavily rely on the design and engineering of electrolytes. However, current schemes for molecular design and recipe optimization of electrolytes lack an effective computational-experimental closed loop and often fall short in accurately predicting diverse electrolyte formulation properties. In this work, we introduce Uni-ELF, a novel multi-level representation learning framework to advance electrolyte design. Our approach involves two-stage pretraining: reconstructing three-dimensional molecular structures at the molecular level using the Uni-Mol model, and predicting statistical structural properties (e.g., radial distribution functions) from molecular dynamics simulations at the mixture level. Through this comprehensive pretraining, Uni-ELF is able to capture intricate molecular and mixture-level information, which significantly enhances its predictive capability. As a result, Uni-ELF substantially outperforms state-of-the-art methods in predicting both molecular properties (e.g., melting point, boiling point, synthesizability) and formulation properties (e.g., conductivity, Coulombic efficiency). Moreover, Uni-ELF can be seamlessly integrated into an automatic experimental design workflow. We believe this innovative framework will pave the way for automated AI-based electrolyte design and engineering. |
| title | Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design |
| topic | Chemical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2407.06152 |